Improving mentorship for residency applications: insights from the Canadian match mentorship program
Bibliographic record
Abstract
BACKGROUND: The Canadian Federation of Medical Students Match Mentorship Program (CFMS-MMP) pairs residency applicants with residents or fellows across Canada based on specialty, school of interest, and social or application-related factors. This quality improvement study examined program strengths, areas for improvement, and valued mentorship characteristics. METHODS: A multi-institutional, cross-sectional survey was distributed to program participants following field testing. The 20-item survey assessed perceptions of the program, preferred pairing criteria, and valued mentorship qualities. Quantitative data were analyzed descriptively, while qualitative responses underwent content analysis. RESULTS: Of 291 final-year medical students and 364 mentors enrolled, 107 mentees and 132 mentors completed the survey. Most mentees (85.4%) prioritized specialty as their top matching criterion, followed by school (10.1%) and demographic concordance (5.6%). Most mentors (70.5%) and mentees (70.1%) agreed or strongly agreed the program was valuable. Many mentors (87.9%) wished to participate again, and 83.2% of mentees were interested in becoming mentors in subsequent iterations. Mentees valued interview preparation (83.2%), mentor communication (82.2%), and availability (80.4%), while mentors most strongly valued the mentee’s willingness to engage meaningfully (84.8%). Gender and racial or ethnic concordance were less valued. Qualitative feedback highlighted flexibility, self-directed structure, and tailored matching as strengths, while proximity to application deadlines and inconsistent mentee engagement were challenges. CONCLUSIONS: The CFMS-MMP provided valued mentorship through flexible, interest-aligned pairings. The implementation of structured check-ins with pairs and mentorship workshops for mentors may improve engagement. Matching by specialty and communication style was preferred over demographic concordance, offering insights for optimizing future mentorship initiatives. Specialty and institutional alignment may be more important than demographic concordance for senior medical students.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".